This is a dense post. Feldman and March, in 1981, wrote “Information in Organizations as Signal and Symbol”. And it makes good predictions about what a management scientist type would say about the purpose of information in an organization. Indeed, just last month, I hyped Carl Anderson’s 2015 original position yet again, in the framing of information as assisting learning. Feldman and March are cited by another piece that’s been weighing heavily since February. Alvesson and Spicer’s 2012 hit “A Stupidity-Based Theory of Organizations” explains why seemingly intelligent people pretend to be dumber than they are. Please don’t misinterpret this passage. It’s not the case that everybody is stupid. Sometimes people act dumber because they have to go-along-to-get-along. Are you[…]

Who do you trust to manage your attention? Because now that the news cycle has surfaced Cambridge Analytica issue – that’s the real thesis question. Let me explain. How the Newsfeed manages your attention I really can’t understate just how powerful amplified engagement really is. When you overlay the like/share verbs on top of a network of individuals who all have something in common, or who procure people who have something in common, you get some pretty strong effects. Don’t believe me? Just check out the clothing in your drawers and the items in your fridge. You, my friend, are an outcome of considerable social contagion effects. Facebook’s newsfeed algorithm shelters you from a power law distribution of content that the[…]

There’s a quote from The Office (US) [Season 6, episodes 5/6, “Launch Party”]: Michael: Okay, okay, what’s better? A medium amount of good pizza? Or all you can eat of pretty good pizza? All: Medium amount of good pizza. Kevin: Oh no, it’s bad. It’s real bad. It’s like eating a hot circle of garbage. The launch in that episode was the ill fated “Dunder Mifflin Infinity”, and while the reference in the passage is to the pizza that Michael Scott had ordered, it may as well been referring to the website. For many reasons, people tend to build all you can eat hot circles of garbage, instead of a medium amount of pretty good pizza. Minimum Viable Product and[…]

Do you like new technology? Chances are that if you’re reading this space, you do. I like new technology too. I don’t like hype as much. I get suspicious when people go out of their way to inflate expectations deliberately in advance of a promise that they know, full well, it can’t deliver. Whether you’re buying for yourself, your home, or your organization, you want to invest in technology that’s likely to have a return, but not such a diminished return that you derive absolutely no competitive advantage or learning from it. There’s a balance there between the fear of losing too much and the greed of unfair advantage. To understand why these feeling develop, it helps to understand why[…]

I was 28 and sleepless when I encountered a marketing version of the logistic function. It was beautiful. It’s one of those things you’re taught about in one context, and when you’re shown it from another angle, it expands your mind. It was like discovering Pi for the first time. I could use it to check the assumptions of a market penetration forecast, and substitute my own estimates for others. I felt empowered and delirious from being able to produce a solid forecast. It became a tool as useful as btau or the crosstab. There’s a part of that math, a variable called saturation, that worried me from the outset. Saturation is the maximum percentage of adoption that a market[…]

Bart Gajderowicz delivered a great talk at Machine Intelligence Toronto about how people go through stages in accomplishing a goal [1]. The talk was about homelessness and AI approaches to public policy. I instantly saw a connection to all sorts of tensions that people endure when they set out on a goal. To distill the concept, let’s start off with the idea that people have goals, people have emotions, and that time moves forward. As people make progress towards their goals, their emotions change over time. They start off in a good mood, in a state of uninformed optimism. Then, as negative information overwhelms their ignorance, they enter into a state of informed pessimism. So much negative information builds up[…]

Ben Thompson calls culture the accumulation of decisions. Assume that it’s true. How do decisions at a tech startup come into being in the first place? A startup can be instantiated with the business plan. And if you take a Beinhocker (2006, The Origin of Wealth) approach to it, you may believe that there’s a Library of Smith which contains every single business plan that’s possible. There are trillions upon trillions of potential business plans. And management is pretty much reduced to a machine that is able to execute the plan to generate wealth. Everything that has potential is possible at the beginning and assume competent management. (Image related – a bit esoteric*). In the context of a startup, a[…]

Previously, I argued that you should look at the Q4-2016 VR sales figures closely and then make decisions about whether to jump in. Some figures are in. SuperData Research, a technology research firm, estimated that Oculus had sold 360,000 headsets and HTC 450,000 since their products went on sale in March and June, respectively. Both of those headsets require high-end PCs with powerful processors. The firm estimated that Sony, which began selling a virtual reality headset in October, has sold about 750,000. — NYtimes Jan 8/2017 Those aren’t encouraging install bases. Obligatory Gartner Hype Cycle image: Consolidation is a long ways off. Facebook, has deep pockets and can sustain a long chasm crossing. The legal issues with Zenimax are a distraction. This is[…]

“A study at Ball State University’s Center for Business and Economic Research last year found that trade accounted for just 13 percent of America’s lost factory jobs. The vast majority of the lost jobs — 88 percent — were taken by robots and other homegrown factors that reduce factories’ need for human labor.” – AP Canada’s labour force is around 19.6 million people, of which 18.2 million people are employed. Together, they worked something like 2.4 billion hours that month. In December 2016, something like 1.7 million Canadians worked about 240 million hours in manufacturing.  Roughly. Because of seasonal adjustments and different data at different times. And error. In terms of our working lives in Canada, collectively, manufacturing is about 10% of[…]